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» Using Machine Learning to Guide Architecture Simulation
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ICML
2008
IEEE
14 years 9 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
CORR
2004
Springer
122views Education» more  CORR 2004»
13 years 8 months ago
Evolution of a Subsumption Architecture Neurocontroller
An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. T...
Julian Togelius
AAAI
1994
13 years 9 months ago
GENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the mill...
Andrew J. Davenport, Edward P. K. Tsang, Chang J. ...
AIEDAM
1998
87views more  AIEDAM 1998»
13 years 7 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh
WCAE
2006
ACM
14 years 2 months ago
Web memory hierarchy learning and research environment
Learning the various structures and levels of memory hierarchy by means of conventional procedures is a complex subject. A memory hierarchy environment (Web-MHE) was proposed and ...
José Leandro D. Mendes, Luiza M. N. Coutinh...